SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2401–2425 of 15113 papers

TitleStatusHype
Antifragile Perimeter Control: Anticipating and Gaining from Disruptions with Reinforcement Learning—0
Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated PoliciesCode0
Align Your Intents: Offline Imitation Learning via Optimal Transport—0
Offline Multi-task Transfer RL with Representational Penalization—0
A Critical Evaluation of AI Feedback for Aligning Large Language ModelsCode2
Self-evolving Autoencoder Embedded Q-Network—0
Programmatic Reinforcement Learning: Navigating Gridworlds—0
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks—0
Policy Learning for Off-Dynamics RL with Deficient SupportCode1
Modelling crypto markets by multi-agent reinforcement learningCode0
Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference AdjustmentCode1
Jack of All Trades, Master of Some, a Multi-Purpose Transformer AgentCode2
Performative Reinforcement Learning in Gradually Shifting EnvironmentsCode0
Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation—0
Steady-State Error Compensation for Reinforcement Learning with Quadratic Rewards—0
How does Your RL Agent Explore? An Optimal Transport Analysis of Occupancy Measure Trajectories—0
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption—0
Exploiting Estimation Bias in Clipped Double Q-Learning for Continous Control Reinforcement Learning Tasks—0
Discovering Command and Control (C2) Channels on Tor and Public Networks Using Reinforcement Learning—0
Hybrid Inverse Reinforcement LearningCode1
Conservative and Risk-Aware Offline Multi-Agent Reinforcement LearningCode0
Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea—0
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models—0
Intelligent Agricultural Management Considering N_2O Emission and Climate Variability with Uncertainties—0
Optimal Task Assignment and Path Planning using Conflict-Based Search with Precedence and Temporal Constraints—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified